Triple
T33389085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jcovden |
E854995
|
entity |
| Predicate | regulatoryLabelIncludesWarningFor |
P2399
|
FINISHED |
| Object | thrombosis with thrombocytopenia syndrome |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: thrombosis with thrombocytopenia syndrome | Statement: [Jcovden, regulatoryLabelIncludesWarningFor, thrombosis with thrombocytopenia syndrome]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regulatoryLabelIncludesWarningFor Context triple: [Jcovden, regulatoryLabelIncludesWarningFor, thrombosis with thrombocytopenia syndrome]
-
A.
hasHealthWarningOnPackaging
Indicates that an item’s packaging displays a health-related warning message or symbol.
-
B.
safetyLabel
Indicates that an entity has been assigned a safety-related classification or warning status.
-
C.
warningIncludes
Indicates that a warning message or notice contains or encompasses specific information, elements, or details.
-
D.
mayBeUsedOffLabelFor
Indicates that something (typically a drug or treatment) can be used for a purpose or condition other than its officially approved or intended use.
-
E.
warnsAbout
chosen
Indicates that one entity alerts or cautions another entity about a potential danger, risk, or problem.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f3496d54048190a1cb91fdd7caa6ea |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69fd592e48cc81909d754cc6c4bd99ae |
completed | May 8, 2026, 3:31 a.m. |
| PD | Predicate disambiguation | batch_69fd58b7f9b881909dc099b28d567784 |
completed | May 8, 2026, 3:30 a.m. |
Created at: May 1, 2026, 1:35 a.m.